The occurrence of word inflection raises certain challenges in the context of word predictions using particular language models, such as n-gram language models. Word inflection refers to the modifying of words to encode grammatical information such as tense, number, gender, and so forth. For example, English inflects regular verbs for past tense using the suffix “ed” (as in “talk”→“talked”). Other languages can exhibit higher levels of word inflection. Romance languages, such as French, have more overt inflection due to complex verb conjugation and gender declension. Agglutinative languages, such as Finnish, have even higher levels of inflection, as a separate inflected form may be needed for each grammatical category.
While one conventional solution to this problem has been to partition words according to morphological information, such approaches do not parsimoniously translate to recurrent neural network language models (RNNLMs) due to the separate histories required for each stem and suffix category considered during operation.